Locating Evacuation Centers Optimally in Path and Cycle Networks

Authors Robert Benkoczi, Binay Bhattacharya, Yuya Higashikawa, Tsunehiko Kameda, Naoki Katoh, Junichi Teruyama



PDF
Thumbnail PDF

File

OASIcs.ATMOS.2021.13.pdf
  • Filesize: 1.08 MB
  • 19 pages

Document Identifiers

Author Details

Robert Benkoczi
  • Department of Mathematics and Computer Science, University of Lethbridge, Canada
Binay Bhattacharya
  • School of Computing Science, Simon Fraser University, Burnaby, Canada
Yuya Higashikawa
  • Graduate School of Information Science, University of Hyogo, Kobe, Japan
Tsunehiko Kameda
  • School of Computing Science, Simon Fraser University, Burnaby, Canada
Naoki Katoh
  • Graduate School of Information Science, University of Hyogo, Kobe, Japan
Junichi Teruyama
  • Graduate School of Information Science, University of Hyogo, Kobe, Japan

Cite As Get BibTex

Robert Benkoczi, Binay Bhattacharya, Yuya Higashikawa, Tsunehiko Kameda, Naoki Katoh, and Junichi Teruyama. Locating Evacuation Centers Optimally in Path and Cycle Networks. In 21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021). Open Access Series in Informatics (OASIcs), Volume 96, pp. 13:1-13:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/OASIcs.ATMOS.2021.13

Abstract

We present dynamic flow algorithms to solve the k-sink problem whose aim is to locate k sinks (evacuation centers) in such a way that the evacuation time of the last evacuee is minimized. In the confluent model, the evacuees originating from or passing through a vertex must evacuate to the same sink, and most known results on the k-sink problem adopt the confluent model. When the edge capacities are uniform (resp. general), our algorithms for non-confluent flow in the path networks run in O(n + k² log² n) (resp. O(n log(n) + k² log⁵ n)) time, where n is the number of vertices. Our algorithms for cycle networks run in O(k²n log² n) (resp. O(k²n log⁵ n)) time, when the edge capacities are uniform (resp. general).

Subject Classification

ACM Subject Classification
  • Theory of computation → Data structures design and analysis
Keywords
  • Efficient algorithms
  • facility location
  • minmax sink
  • evacuation problem
  • dynamic flow in network

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. P.K. Agarwal and M. Sharir. Efficient algorithms for geometric optimization. ACM Computing Surveys, 30(4):412-458, 1998. Google Scholar
  2. G.P. Arumugam, J. Augustine, M. Golin, Y. Higashikawa, N. Katoh, and P. Srikanthan. Optimal evacuation flows on dynamic paths with general edge capacities. CoRR abs/1606.07208, pages 1-37, 2016. Google Scholar
  3. R. Benkoczi and R. Das. The min-max sink location problem on dynamic cycle networks. Unpublished manuscript, October 2018. Google Scholar
  4. B. Bhattacharya, M. Golin, Y. Higashikawa, T. Kameda, and N. Katoh. Improved algorithms for computing k-sink on dynamic flow path networks. In Proc. Algorithms and Data Structures Symp., Springer-Verlag, LNCS 10389, pages 133-144, 2017. Google Scholar
  5. B. Bhattacharya and T. Kameda. Improved algorithms for computing minmax regret sinks on path and tree networks. Theoretical Computer Science, 607:411-425, November 2015. Google Scholar
  6. D. Chen and M.J. Golin. Minmax centered k-partitioning of trees and applications to sink evacuation with dynamic confluent flows. In CoRR abs/1803.09289, 2018. Google Scholar
  7. R. Das. Minmax sink location problem on dynamic cycle networks. Master’s thesis, University, of Lethbridge, Lethbridge, Canada, 2018. Google Scholar
  8. L.R. Ford and A.D.R. Fulkerson. Constructing maximal dynamic flows from static flows. Operations Research, 6(3):419-433, 1958. Google Scholar
  9. M.J. Golin, H. Khodabande, and B. Qin. Non-approximability and polylogarithmic approximations of the single-sink unsplittable and confluent dynamic flow problems. In Proc. 28th Int'l Symp. on Algorithms and Computation (ISAAC), pages 41:1-41:13, 2017. Google Scholar
  10. H.W. Hamacher and S.A. Tjandra. Mathematical modelling of evacuation problems: a state of the art. in: Pedestrian and Evacuation Dynamics, Springer Verlag,, pages 227-266, 2002. Google Scholar
  11. Y. Higashikawa, M.J. Golin, and N. Katoh. Multiple sink location problems in dynamic path networks. Theoretical Computer Science, 607(1):2-15, 2015. Google Scholar
  12. Y. Higashikawa and N. Katoh. A survey on facility location problems in dynamic networks. The Review of Socionetwork Strategies, 13:163-208, September 2019. Google Scholar
  13. B. Hoppe and É. Tardos. The quickest transshipment problem. Mathematics of Operations Research, 25(1):36-62, 2000. Google Scholar
  14. N. Kamiyama. Studies on Quickest Flow Problems in Dynamic Networks and Arborescence Problems in Directed Graphs: A Theoretical Approach to Evacuation Planning in Urban Areas. PhD thesis, Kyoto University, 2005. Google Scholar
  15. S. Mamada, K. Makino, and S. Fujishige. Optimal sink location problem for dynamic flows in a tree network. IEICE Trans. Fundamentals, E85-A:1020-1025, 2002. Google Scholar
  16. N. Megiddo. Combinatorial optimization with rational objective functions. Math. Oper. Res., 4:414-424, 1979. Google Scholar
  17. M. Skutella. An introduction to network flows over time. In Research Trends in Combinatorial Optimization, W. Cook, L. Lovasz, and J.Vygen, Eds., pages 451-482. Springer Verlag, 2009. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail